apollo_mdcev: Calculates MDCEV likelihoods.
In byu-transpolab/apollo: Tools for Choice Model Estimation and Application

Calculates the likelihood of a Multiple Discrete Continuous Extreme Value (MDCEV) model.

1	apollo_mdcev(mdcev_settings, functionality)

mdcev_settings

List of settings for the MDCEV model. It must include the following.

V: Named list. Utilities of the alternatives. Names of elements must match those in argument 'alternatives'.
alternatives: Character vector. Names of alternatives, elements must match the names in list 'V'.
alpha: Named list. Alpha parameters for each alternative, including for any outside good. As many elements as alternatives.
gamma: Named list. Gamma parameters for each alternative, excluding any outside good. As many elements as inside good alternatives.
sigma: Numeric scalar. Scale parameter of the model extreme value type I error.
cost: Named list of numeric vectors. Price of each alternative. One element per alternative, each one as long as the number of observations or a scalar. Names must match those in alternatives.
avail: Named list. Availabilities of alternatives, one element per alternative. Names of elements must match those in argument 'alternatives'. Value for each element can be 1 (scalar if always available) or a vector with values 0 or 1 for each observation.
continuousChoice: Named list of numeric vectors. Amount of consumption of each alternative. One element per alternative, as long as the number of observations or a scalar. Names must match those in alternatives.
budget: Numeric vector. Budget for each observation.
minConsumption: Named list of scalars or numeric vectors. Minimum consumption of the alternatives, if consumed. As many elements as alternatives. Names must match those in alternatives.
outside: Character. Optional name of the outside good.
rows: Logical vector. Consideration of rows in the likelihood calculation, FALSE to exclude. Length equal to the number of observations (nObs). Default is "all", equivalent to rep(TRUE, nObs).
componentName: Character. Name given to model component.
nRep: Numeric scalar. Number of simulations of the whole dataset used for forecasting. The forecast is the average of these simulations. Default is 100.
rawPrediction: Logical scalar. TRUE for prediction to be returned at the draw level (a 3-dim array). FALSE for prediction to be returned averaged across draws. Default is FALSE.

functionality

Character. Can take different values depending on desired output.

"estimate" Used for model estimation.
"prediction" Used for model predictions.
"validate" Used for validating input.
"zero_LL" Used for calculating null likelihood.
"conditionals" Used for calculating conditionals.
"output" Used for preparing output after model estimation.
"raw" Used for debugging.

The returned object depends on the value of argument functionality as follows.

"estimate": vector/matrix/array. Returns the probabilities for the observed consumption for each observation.
"prediction": A matrix with one row per observation, and columns indicating means and s.d. of continuous and discrete predicted consumptions.
"validate": Same as "estimate", but it also runs a set of tests to validate the function inputs.
"zero_LL": Not implemented. Returns a vector of NA with as many elements as observations.
"conditionals": Same as "estimate"
"output": Same as "estimate" but also writes summary of input data to internal Apollo log.
"raw": Same as "estimate"